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Automatic foreground extraction based on difference of Gaussian.

Yuan Y, Liu Y, Dai G, Zhang J, Chen Z - ScientificWorldJournal (2014)

Bottom Line: In our algorithm, DoG is employed to find the candidate keypoints of an input image in different color layers.Finally, Normalized cut (Ncut) is used to segment an image into several regions and locate the foreground with the number of keypoints in each region.Experiments on the given image data set demonstrate the effectiveness of our algorithm.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai 200237, China.

ABSTRACT
A novel algorithm for automatic foreground extraction based on difference of Gaussian (DoG) is presented. In our algorithm, DoG is employed to find the candidate keypoints of an input image in different color layers. Then, a keypoints filter algorithm is proposed to get the keypoints by removing the pseudo-keypoints and rebuilding the important keypoints. Finally, Normalized cut (Ncut) is used to segment an image into several regions and locate the foreground with the number of keypoints in each region. Experiments on the given image data set demonstrate the effectiveness of our algorithm.

Show MeSH
Flowchart of the basic procedure of FMDOG. The result of difference of Gaussian (DoG) and Normalized cut (Ncut) is combined by the keypoints filter. The last image is the foreground we extract in this model image.
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Related In: Results  -  Collection


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fig1: Flowchart of the basic procedure of FMDOG. The result of difference of Gaussian (DoG) and Normalized cut (Ncut) is combined by the keypoints filter. The last image is the foreground we extract in this model image.

Mentions: The basic procedures to locate the foreground are illustrated as in Figure 1.


Automatic foreground extraction based on difference of Gaussian.

Yuan Y, Liu Y, Dai G, Zhang J, Chen Z - ScientificWorldJournal (2014)

Flowchart of the basic procedure of FMDOG. The result of difference of Gaussian (DoG) and Normalized cut (Ncut) is combined by the keypoints filter. The last image is the foreground we extract in this model image.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4127285&req=5

fig1: Flowchart of the basic procedure of FMDOG. The result of difference of Gaussian (DoG) and Normalized cut (Ncut) is combined by the keypoints filter. The last image is the foreground we extract in this model image.
Mentions: The basic procedures to locate the foreground are illustrated as in Figure 1.

Bottom Line: In our algorithm, DoG is employed to find the candidate keypoints of an input image in different color layers.Finally, Normalized cut (Ncut) is used to segment an image into several regions and locate the foreground with the number of keypoints in each region.Experiments on the given image data set demonstrate the effectiveness of our algorithm.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai 200237, China.

ABSTRACT
A novel algorithm for automatic foreground extraction based on difference of Gaussian (DoG) is presented. In our algorithm, DoG is employed to find the candidate keypoints of an input image in different color layers. Then, a keypoints filter algorithm is proposed to get the keypoints by removing the pseudo-keypoints and rebuilding the important keypoints. Finally, Normalized cut (Ncut) is used to segment an image into several regions and locate the foreground with the number of keypoints in each region. Experiments on the given image data set demonstrate the effectiveness of our algorithm.

Show MeSH